Scaling and Uncertainty Analysis in Ecology

Wu, Jiang; Jones, Kevin B.; Li, Harbin; Loucks, Orie L.

doi:10.1007/1-4020-4663-4

Cited by 114 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The detection of cross-scale effects of the environment on species occurrence proves that our analysis design gives a reasonable picture of the intrinsic scales (Wu & Li, 2006) at which processes may operate in shaping species distribution. Our approach integrates multi-level processes (Peters et al, 2007).…”

Section: Thresholds In Local and Connectivity Effects: Implicationsmentioning

confidence: 73%

Cross‐scale effects of structural and functional connectivity in pond networks on amphibian distribution in agricultural landscapes

et al. 2019

View full text Add to dashboard Cite

Connectivity in pond ecosystems is essential for amphibian population persistence. To test the role of connectivity, a diversity of scales accounting for the different processes underlying species distribution needs to be considered. Several approaches exist for accounting for the potential connectivity, mainly based on structural versus functional methods. Based on occupancy data, our aim was to assess the relative power of connectivity metrics in explaining amphibian distribution when accounting for cross‐scale effects from pond to network scales, and to identify network features relevant to amphibian conservation in agricultural landscapes in the Seine‐et‐Marne department (east of Paris, France). We propose an original analytical framework that allows examining different spatial scales of influence, with a focus on connectivity. We analyse the distribution of three amphibian species (Bufo bufo, Rana dalmatina, and Lissotriton vulgaris) in relation to various pond conditions (fish presence, water quality, and nearby woodland habitat) as well as connectivity using three connectivity approaches: (1) structural, (2) area‐functional based on potential migration areas, and (3) path‐functional based on the least accumulated cost paths. Values of landscape resistance were assigned according to a biological risk‐based approach that is proposed as an enhanced, transparent expert‐based approach. We further investigated cross‐scale effects in amphibian responses to the environment by assessing the relative importance of pond and network characteristics as well as their interactions. Despite some species‐specific characteristics, the area‐functional approach of connectivity was generally the most effective in explaining species distribution by emphasising the relevance of potential migration areas as conservation units. Although pond conditions usually had a greater influence than network connectivity, we highlighted important cross‐scale and threshold effects in species’ response to local conditions, to pond connectivity, and to the availability of potentially suitable terrestrial habitats in the networks. Our cross‐scale approach accounts for scale‐dependent processes that potentially underlie amphibian distribution such as habitat selection and metapopulation dynamics. Both structural and functional connectivity should be considered as complementary features of pond connectivity when dispersal traits are likely to be less limiting. From a management perspective, our results encourage conservation plans to combine functional network‐centred strategies with the preservation of local conditions. We give guidelines as to the features of such networks to help maintain amphibian populations in agricultural landscapes.

show abstract

Section: Thresholds In Local and Connectivity Effects: Implicationsmentioning

confidence: 73%

Cross‐scale effects of structural and functional connectivity in pond networks on amphibian distribution in agricultural landscapes

et al. 2019

View full text Add to dashboard Cite

show abstract

“…This is reflected in the broad range of model-based scaling-up methods. Reviews of ecological model-based scaling methods are available for general ecology (Rastetter, Aber, Peters, Ojima, & Burke, 2003;Urban, 2005;Urban et al, 1999;Wu et al, 2006), species distribution models (Miller et al, 2004), landscape research (Lischke et al, 2007), forest ecosystem management (Seidl et al, 2013) and dynamic vegetation models (Snell et al, 2014); thus, we only give a short overview here (see also (Smith et al, 2008).…”

Section: In-model Scalingmentioning

confidence: 99%

“…Averaged or 'effective' parameters (Wu et al, 2006) can sometimes be derived from the small scale for the large scale and directly used as input to the original small-scale model to generate largescale output (Cipriotti et al, 2016). This only works in the absence of scale-dependent processes or interactions at the large scale.…”

Section: In-model Scalingmentioning

confidence: 99%

Scaling methods in ecological modelling

2020

View full text Add to dashboard Cite

Scaling is ubiquitous and persistent in ecology. Following the acclaimed concept of pattern and scale (Levin, 1992), patterns and processes at a certain spatial or temporal scale or organizational level emerge from patterns and processes at finer scales or levels and these, in turn, are influenced by the large-scale patterns (Figure 1, Lischke, Löffler, Thornton, & Zimmermann, 2007). Due to this, scaling, that is, changing from one scale to another, is not always straightforward, and sometimes can cause problems due to scale breaks, nonlinearities, feedbacks and heterogeneity in such patternprocess relationships (Snell et al., 2014). Additionally, scaling is sometimes not explicit, and confusion in terminology adds to scaling-related problems. Here, we (1) address scaling terminology; (2) define three categories of scaling approaches, (a) pre-model scaling, (b) in-model scaling and (c) post-model scaling; and (3) explore examples, problems, and, where available, potential solutions in each category. We also elaborate our main claim that modelling is often confronted with scaling challenges, because modelling-directly or indirectly-always implies scaling.

show abstract

“…Generally, upscaling aggregates local data by averaging them to a larger scale. Thereby, the upscaling of a local to global scale always has a loss of information while downscaling may not represent the local scale adequately 78 . The challenge is to find representative indicators that reflect the transformation of data.…”

Section: Production Volume Estimationmentioning

confidence: 99%